یک رویکرد فازی برای ارزیابی تامین کنندگان و انتخاب در مدیریت زنجیره تامین
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|19122||2006||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 102, Issue 2, August 2006, Pages 289–301
This paper is aimed to present a fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. In general, many quantitative and qualitative factors such as quality, price, and flexibility and delivery performance must be considered to determine suitable suppliers. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to deal with the supplier selection problems in the supply chain system. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all suppliers by calculating the distances to the both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. Finally, an example is shown to highlight the procedure of the proposed method at the end of this paper. This paper shows that the proposed model is very well suited as a decision-making tool for supplier selection decisions.
Recently, supply chain management and the supplier (vendor) selection process have received considerable attention in the business-management literature. During the 1990s, many manufacturers seek to collaborate with their suppliers in order to upgrade their management performance and competitiveness (Ittner et al., 1999; Shin et al., 2000). The material flow in a supply chain is shown in Fig. 1. The purchasing function is increasingly seen as a strategic issue in organizations. Buyer and supplier relationships in manufacturing enterprises have received a great deal of attention. When it is built on long-term relationships, a company's supply chain creates one of the strongest barriers to entry for competitors (Briggs, 1994; Choi and Hartley, 1996). In other words, once a supplier becomes part of a well-managed and established supply chain, this relationship will have a lasting effect on the competitiveness of the entire supply chain. Therefore, the supplier selection problem has become one of the most important issues for establishing an effective supply chain system. The overall objective of supplier selection process is to reduce purchase risk, maximize overall value to the purchaser, and build the closeness and long-term relationships between buyers and suppliers (Monczka et al., 1998). Full-size image (20 K) Fig. 1. Material flow in supply chain. Figure options In supply chains, coordination between a manufacturer and suppliers is typically a difficult and important link in the channel of distribution. Many models have been developed for supplier selection decisions are based on rather simplistic perceptions of decision-making process (Boer et al., 1998; Lee et al., 2001). Most of these methods do not seem to address the complex and unstructured nature and context of many present-day purchasing decisions (Boer et al., 1998). In fact, many existing decision models only quantities criteria are considered for supplier selection. However, several influence factors are often not taken into account in the decision making process, such as incomplete information, additional qualitative criteria and imprecision preferences. According to the vast literature on supplier selection (Boer et al., 1998; Choi and Hartley, 1996; Weber et al., 1991), we conclude that some properties are worth considering when solving the decision-making problem for supplier selection. First, the criteria may consider quantitative as well as qualitative dimensions (Choi and Hartley, 1996; Dowlatshahi, 2000; Verma and Pullman, 1998; Weber et al., 1991 and Weber et al., 1998). In general, these objectives among these criteria are conflicted. A strategic approach towards supplier selection may further emphasize the need to consider multiple criteria (Donaldson, 1994; Ellram, 1992; Swift, 1995). Second, several decision-makers are very often involved in the decision process for supplier selection (Boer et al., 1998). Third, decision-making is often influenced by uncertainty in practice. An increasing number of supplier decisions can be characterized as dynamic and unstructured. Situations are changing rapidly or are uncertain and decision variables are difficult or impossible to quantify (Cook, 1992). Fourth, the types of decision models can be divided into compensatory and non-compensatory methods (Boer et al., 1998; Ghodsypour and O’Brien, 1998; Roodhooft and Konings, 1996). The compensatory decision models leading to an optimal solution for dealing with supplier selection problems. The non-compensatory methods are that use a score of an alternative on a particular criterion can be compensated by high scores on other criteria. From the literature it can be concluded that in supplier selection the classic concept of “optimality” may not always be the most appropriate model (Boer et al., 1998). Overall speaking, we can conclude that supplier selection may involve several and different types of criteria, combination of different decision models, group decision-making and various forms of uncertainty. It is difficult to find the best way to evaluate and select supplier, and companies use a variety of different methods to deal with it. Therefore, the most important issue in the process of supplier selection is to develop a suitable method to select the right supplier. In essential, the supplier selection problem in supply chain system is a group decision-making under multiple criteria. The degree of uncertainty, the number of decision makers and the nature of the criteria those have to be taken into account in solving this problem. In classical MCDM methods, the ratings and the weights of the criteria are known precisely (Delgado et al., 1992; Hwang and Yoon, 1981; Kaufmann and Gupta, 1991). A survey of the methods has been presented in Hwang and Yoon (1981). Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), one of the known classical MCDM methods, may provide the basis for developing supplier selection models that can effectively deal with these properties. It bases upon the concept that the chosen alternative should have the shortest distance from the Positive Ideal Solution (PIS) and the farthest from the Negative Ideal Solution (NIS). Under many conditions, crisp data are inadequate to model real-life situations. Since human judgements including preferences are often vague and cannot estimate his preference with an exact numerical value. A more realistic approach may be to use linguistic assessments instead of numerical values. In other words, the ratings and weights of the criteria in the problem are assessed by means of linguistic variables (Bellman and Zadeh, 1970; Chen, 2000; Delgado et al., 1992; Herrera et al., 1996; Herrera and Herrera-Viedma, 2000). In this paper, we further extended to the concept of TOPSIS to develop a methodology for solving supplier selection problems in fuzzy environment (Chen, 2000). Considering the fuzziness in the decision data and group decision-making process, linguistic variables are used to assess the weights of all criteria and the ratings of each alternative with respect to each criterion. We can convert the decision matrix into a fuzzy decision matrix and construct a weighted-normalized fuzzy decision matrix once the decision-makers’ fuzzy ratings have been pooled. According to the concept of TOPSIS, we define the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS). And then, a vertex method is applied in this paper to calculate the distance between two fuzzy ratings. Using the vertex method, we can calculate the distance of each alternative from FPIS and FNIS, respectively. Finally, a closeness coefficient of each alternative is defined to determine the ranking order of all alternatives. The higher value of closeness coefficient indicates that an alternative is closer to FPIS and farther from FNIS simultaneously. The paper is organized as follows. Next section introduces the basic definitions and notations of the fuzzy numbers and linguistic variables. In Section 3, we present a fuzzy decision-making method to cope with the supplier selection problem. And then, the proposed method is illustrated with an example. Finally, some conclusions are pointed out at the end of this paper.
نتیجه گیری انگلیسی
Many practitioners and researchers have presented the advantages of supply chain management. In order to increase the competitive advantage, many companies consider that a well-designed and implemented supply chain system is an important tool. Under this condition, building on the closeness and long-term relationships between buyers and suppliers is critical success factor to establish the supply chain system. Therefore, supplier selection problem becomes the most important issue to implement a successful supply chain system. In general, supplier selection problems adhere to uncertain and imprecise data, and fuzzy-set theory is adequate to deal with them. In a decision-making process, the use of linguistic variables in decision problems is highly beneficial when performance values cannot be expressed by means of numerical values. In other words, very often, in assessing of possible suppliers with respect to criteria and importance weights, it is appropriate to use linguistic variables instead of numerical values. Due to the decision-makers’ experience, feel and subjective estimates often appear in the process of supplier selection problem, an extension version of TOPSIS in a fuzzy environment is proposed in this paper. The fuzzy TOPSIS method can deal with the ratings of both quantitative as well as qualitative criteria and select the suitable supplier effectively. It appears from the foregoing sections that fuzzy TOPSIS method may be a useful additional tool for the problem of supplier selection in supply chain system. In fact, the fuzzy TOPSIS method is very flexible. According to the closeness coefficient, we can determine not only the ranking order but also the assessment status of all possible suppliers. Significantly, the proposed method provides more objective information for supplier selection and evaluation in supply chain system. The systematic framework for supplier selection in a fuzzy environment presented in this paper can be easily extended to the analysis of other management decision problems. However, improving the approach for solving supplier selection problems more efficiently and developing a group decision-support system in a fuzzy environment can be considered as a topic for future research.